Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature
Constraining Climate Sensitivity from the Seasonal Cycle in Surface Temperature
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DOI：
10.1175/jcli3865.1
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发表时间：
2006-09
影响因子：
4.9
通讯作者：
R. Knutti;G. Meehl;M. Allen;D. Stainforth
中科院分区：
文献类型：
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作者：
R. Knutti;G. Meehl;M. Allen;D. Stainforth
摘要气候敏感性的估计范围几十年来一直保持不变，导致在温室气体浓度增加的情况下对未来气候的长期预测存在很大的不确定性。在这里，来自climateprediction.net项目的气候模式模拟的数千成员集合和神经网络被用来建立气候敏感性和区域温度季节周期幅度之间的关系。大多数高灵敏度的模型被发现高估的季节性周期相比，观察。然后，根据再分析和仪器数据集中当今的季节循环计算气候敏感性的概率密度函数。根据对所使用的模型和数据集的一些假设，发现气候敏感性不太可能（5%的概率）低于1.5-2 K或高于约5-6.5 K，敏感性在3和3.5 K之间最一致。这个范围比大多数问题都要窄。
Abstract The estimated range of climate sensitivity has remained unchanged for decades, resulting in large uncertainties in long-term projections of future climate under increased greenhouse gas concentrations. Here the multi-thousand-member ensemble of climate model simulations from the climateprediction.net project and a neural network are used to establish a relation between climate sensitivity and the amplitude of the seasonal cycle in regional temperature. Most models with high sensitivities are found to overestimate the seasonal cycle compared to observations. A probability density function for climate sensitivity is then calculated from the present-day seasonal cycle in reanalysis and instrumental datasets. Subject to a number of assumptions on the models and datasets used, it is found that climate sensitivity is very unlikely (5% probability) to be either below 1.5–2 K or above about 5–6.5 K, with the best agreement found for sensitivities between 3 and 3.5 K. This range is narrower than most prob...